3,283 research outputs found
Small Covers over Prisms
In this paper we calculate the number of equivariant diffeomorphism classes
of small covers over a prism
Examining A Renormalizable Supersymmetric SO(10) Model
We examine a renormalizable SUSY SO(10) model without fine-tuning. We show
how to construct MSSM doublets and to predict proton decay. We find that in the
minimal set of Yukawa couplings the model is consistent with the experiments,
while including to fit the data there are inconsistencies.Comment: 18 page
Genetic incorporation of D-Lysine into diketoreductase in Escherichia coli cells
D-Lysine has been genetically introduced into diketoreductase in E. coli cells by utilization of an orthogonal Ph tRNA /Lysyl-tRNA synthetase pair. This is the first report on the genetic incoporation of D-amino acids into proteins, which may be generally applicable to a wide variety of applications
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
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